Please use this identifier to cite or link to this item:
https://doi.org/10.3182/20110828-6-IT-1002.00492
DC Field | Value | |
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dc.title | A correlation least-squares method for Hammerstein model identification with ARX and μ-Markov structures | |
dc.contributor.author | Lum, K.-Y. | |
dc.contributor.author | Bernstein, D.S. | |
dc.date.accessioned | 2014-11-28T01:53:06Z | |
dc.date.available | 2014-11-28T01:53:06Z | |
dc.date.issued | 2011 | |
dc.identifier.citation | Lum, K.-Y.,Bernstein, D.S. (2011). A correlation least-squares method for Hammerstein model identification with ARX and μ-Markov structures. IFAC Proceedings Volumes (IFAC-PapersOnline) 18 (PART 1) : 11183-11189. ScholarBank@NUS Repository. <a href="https://doi.org/10.3182/20110828-6-IT-1002.00492" target="_blank">https://doi.org/10.3182/20110828-6-IT-1002.00492</a> | |
dc.identifier.isbn | 9783902661937 | |
dc.identifier.issn | 14746670 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/111513 | |
dc.description.abstract | This paper presents a two-step method for identification of the SISO Hammerstein model, which employs input autocorrelation and input-output cross-correlation functions as data for least-squares estimation. Using separable processes as input signals, the proposed method allows the linear block of a Hammerstein model to be identified up to a multiplicative constant, without a priori knowledge of the nonlinear model structure. Both ARX and μ-Markov structures of the linear block are considered, where the main concern is the accuracy of pole and zero estimates. The correlation least-squares method is compared numerically with a well-known nonlinear least-squares method, which shows that the correlation method is consistently accurate across different nonlinear model structures. © 2011 IFAC. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.3182/20110828-6-IT-1002.00492 | |
dc.source | Scopus | |
dc.subject | ARMA models | |
dc.subject | Least-squares estimation | |
dc.subject | Markov parameters | |
dc.subject | Nonlinear models | |
dc.subject | System identification | |
dc.type | Conference Paper | |
dc.contributor.department | TEMASEK LABORATORIES | |
dc.description.doi | 10.3182/20110828-6-IT-1002.00492 | |
dc.description.sourcetitle | IFAC Proceedings Volumes (IFAC-PapersOnline) | |
dc.description.volume | 18 | |
dc.description.issue | PART 1 | |
dc.description.page | 11183-11189 | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Staff Publications |
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